This piece kicks off a miniseries in which we take a look at this question through the eyes of several of our leaders who know firsthand the challenges market data teams face in tackling this question in making technology decisions. Hope you find this conversation as interesting as we did! Stay tuned for future takes on the topic.
In our recent Market Data Matters roundtable, we explored the growing narrative around the ‘death of SaaS’ and why the reality is far more nuanced. The conclusion: AI may change how users interact with software, but it doesn’t remove the need for the structured systems, workflows and operational foundations underneath.
That conversation naturally led to another question.
If AI is making software faster and cheaper to build, how should firms now think about the build versus buy decision?
We sat down with Amjad Zogbhi, CTO at TRG Screen, to get his perspective on what is actually changing, where firms risk overestimating AI’s impact and why the future may be less about ‘build versus buy’ and more about how the two increasingly work together.
The biggest change is speed.
The pace of development is completely different now. We used to think in one, two or even three-year cycles. Today, things are moving so quickly that firms have to plan over much shorter time horizons.
The economics of building software have changed as well. Things that might previously have taken months (or longer) can now be built in weeks. AI has dramatically lowered the time and cost involved in developing new capabilities.
That’s obviously a huge opportunity, but it also changes the risk profile. Because development is moving so much faster, firms can now build things internally before properly thinking through whether they’re genuinely strategic or sustainable in the long term.
It’s also changing how product and technology teams work together. The old model was much more process-driven. Product teams would define requirements and development teams would go away and build them. Now the process is becoming much more collaborative because you can iterate so quickly. Product and technology decisions are becoming much more closely connected.
Yes, but not necessarily in the way people think.
The build versus buy question has always existed. What AI changes is the feasibility side of the equation.
Historically, many firms simply could not justify building complex systems internally because the time, cost and resource requirements were too high. AI lowers that barrier significantly.
The question used to be “can we build this?” Increasingly, the question is now “should we build this?”
We definitely hear more organizations saying: “We can do this with AI, so why should we buy software platforms from external providers?”
But there’s a very big difference between building something that works and building an enterprise-ready platform that is scalable, secure and operationally robust.
Not everything should be built internally, even if AI now makes it technically possible.
If something is genuinely core to your competitive advantage, then it probably makes sense to invest internally and build around that capability. Even then, most firms are not starting from zero. Mature platforms already exist on the market that can significantly accelerate development.
Where firms need to think more carefully is around the operational and support functions that sit underneath the business. Areas like market data management are incredibly complex, but they don’t differentiate the organization competitively.
In those cases, firms need to think carefully about whether it makes sense to rebuild capabilities that specialist providers have already spent years refining.
AI doesn’t just accelerate development for end users. It accelerates development for specialist providers as well.
The same tools available internally to firms are also being used by vendors that already have dedicated product teams, engineering resources, industry expertise and long-term product strategies in place.
That changes the equation significantly.
Firms are not just buying software. They’re buying platforms shaped by years of client experience, evolving vendor requirements and continuous refinement across complex environments.
In many cases, AI may actually widen the gap between specialist providers and internal development teams because those providers are able to apply AI on top of already mature platforms and deep market expertise.
AI dramatically speeds up development, but building something quickly is not the same as building an enterprise-ready platform.
In market data management especially, the hard part is rarely the interface or the extraction layer – the part the users see. It’s the operational complexity underneath; the logic required to manage all of that consistently at scale.
That’s where firms can sometimes overestimate what AI alone is capable of delivering.
You still need strong underlying systems and deep domain expertise to make those platforms reliable over time.
I don’t think the future becomes build versus buy. I actually think it becomes much more buy and build; both blended together.
Core enterprise platforms still matter. They provide the structured foundation underneath everything else. What AI changes is how users interact with those systems.
Users will increasingly build lightweight layers around core platforms themselves; dashboards, workflows, automations, integrations and visualizations tailored to their own needs.
AI will make that customization dramatically faster and easier. The platform becomes the foundation that users build around.
That’s why the future is unlikely to be organizations replacing specialist platforms altogether. It’s more likely to be a blended model where firms buy the core operational foundations from specialist providers, then build and customize around them using AI-enabled tools and workflows.